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correlation and percentages. association between variables can be explored using counts are high counts of bone needles associated with high counts of end scrapers? similar questions can be asked using percent-standardized data - PowerPoint PPT Presentation
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correlation and percentages
• association between variables can be explored using counts– are high counts of bone needles
associated with high counts of end scrapers?
• similar questions can be asked using percent-standardized data– are high proportions of decorated pottery
associated with high proportions of copper bells?
but…• these are different questions with
different implications for formal regression
• percents will show some correlation even if underlying counts do not…– ‘spurious’ correlation (negative)– “closed-sum” effect
case C_v1 C_v2 C_v3 C_v4 C_v5 C_v6 C_v7 C_v8 C_v9 C_v10
1 15 14 94 59 76 13 8 97 10 95
2 35 1 89 95 23 77 14 9 27 43
3 20 96 73 31 90 65 74 60 85 27
4 23 59 7 52 33 83 71 35 57 90
5 36 90 86 15 97 54 52 41 34 3
6 79 2 26 5 11 68 74 44 13 87
7 40 99 28 66 77 23 69 22 63 36
8 95 36 22 75 21 48 95 58 74 68
9 27 0 58 99 32 30 5 5 100 75
10 67 93 98 61 62 94 3 16 43 48
10 vars.5 vars.
3 vars.2 vars.
matrix(round(rnorm(100, 50, 15), nrow=10)))
-1.0 -0.5 0.0 0.5 1.0r
original counts
-1.0 -0.5 0.0 0.5 1.0r
%s (10 vars.)
-1.0 -0.5 0.0 0.5 1.0r
%s (5 vars.)
-1.0 -0.5 0.0 0.5 1.0r
%s (3 vars.)
-1.0 -0.5 0.0 0.5 1.0r
%s (2 vars.)
0 20 40 60 80 100C_V1
0
20
40
60
80
100
C_
V2
0 5 10 15 20P10_V1
0
5
10
15
20
P10
_V2
0 10 20 30 40 50 60 70T5_V1
0
10
20
30
40
T5_
V2
10 20 30 40 50 60 70 80T3_V1
0
10
20
30
40
50
60
70
T3_
V2
10 20 30 40 50 60 70 80 90 100T2_V1
0
10
20
30
40
50
60
70
80
90
T2_
V2
original counts %s 10 vars.
%s 5 vars. %s 3 vars. %s 2 vars.
outliers
• including outliers in regression analyses is usually a bad idea…
• Tukey-line / least squares discrepancies are good red-flag signals
2 4 6 8 10
51
01
5
x2
y2
2 4 6 8 10
51
01
5
x2
y2
2 4 6 8 10
51
01
5
x2
y2
0 50 100 150 200 250
800
850
900
950
100
01
050
110
0
soMort$SO2
soM
ort$
mor
tal
“convex hull trimming”
0 1 2 3 4 5
800
850
900
950
100
01
050
110
0
log(soMort$SO2)
soM
ort$
mor
tal
0 1 2 3 4 5
800
850
900
950
100
01
050
110
0
log(soMort$SO2)
soM
ort$
mor
tal
“convex hull trimming”
> hull1 chull(x, y)
> plot(x, y)
> polygon(x[hull1], y[hull1])
> abline(lm(y[-hull1] ~ x[-hull1]))
0 1 2 3 4 5 6
80
09
00
10
00
11
00
log(soMort$SO2)
soM
ort
$m
ort
al
transformation
transformation
• at least two major motivations in regression analysis:– create/improve a linear relationship– correct skewed distribution(s)
• ex: density of obsidian vs. distance from the quarry:
0 10 20 30 40 50 60 70 80DIST
0
1
2
3
4
5
6D
EN
SIT
Y
0 10 20 30 40 50 60 70 80DIST
0
1
2
3
4
5
6
DE
NS
ITY
Plot of Residuals against Predicted Values
-1 0 1 2 3 4ESTIMATE
-1
0
1
2
RE
SID
UA
L
0 10 20 30 40 50 60 70 80DIST
1
2
3456
DE
NS
ITY
0 10 20 30 40 50 60 70 80DIST
-3
-2
-1
0
1
2
LG
_D
EN
S
LG_DENS log(DENSITY)
old.par par(no.readonly = TRUE)
plot(DIST, DENSITY, log="y")par(old.par)
0 50 100 150 200VAR1
0
50
100
150
200
VA
R2
0 50 100 150 200VAR1
0
50
100
150
200V
AR
2
0 5 10 15VAR1T
0
50
100
150
200
VA
R2
> VAR1T sqrt(VAR1)> plot(VAR1T, VAR2)
transformation summary
• correcting left skew:x4 stronger
x3 strong
x2 mild
• correcting right skew:x weak
log(x) mild
-1/x strong
-1/x2 stronger
“coefficient of determination”
• regression/correlation– the strength of a relationship can be
assessed by seeing how knowledge of one variable improves the ability to predict the other
• if you ignore x, the best predictor of y will be the mean of all y values (y-bar) – if the y measurements are widely
scattered, prediction errors will be greater than if they are close together
• we can assess the dispersion of y values around their mean by:
2)( yyi
y
iy
2)( yyi
2)ˆ( ii yy
2)ˆ( ii yy
2)( yyir2=
• “coefficient of determination” (r2)
• describes the proportion of variation that is “explained” or accounted for by the regression line…
• r2=.5 half of the variation is explained by the regression…
half of the variation in y is explained by variation in x…
vs.
“explaining variance”
range
x
vs.
multiple regression
residuals
• vertical deviations of points around the regression – for case i, residual = yi-ŷi [yi-(a+bxi)]
• residuals in y should not show patterned variation either with x or y-hat
• should be normally distributed around the regression line
• residual error should not be autocorrelated (errors/residuals in y are independent…)
• residuals may show patterning with respect to other variables…
• explore this with a residual scatterplot– ŷ vs. other variables…
• are there suggestions of linear or other kinds of relationships?
• if r2 < 1, some of the remaining variation may be explainable with reference to other variables
• paying close attention to outliers in a residual plot may lead to important insights
• e.g.: outlying residuals from quantities of exotic flint ~ distance from quarries– sites with special access though transport
routes, political alliances…
• residuals from regressions are often the main payoff
Middle Formative,
Basin of Mexico
Formative Basin of Mexico
• settlement survey
• 3 variables recorded from sites:– site size (proxy for population)– amount of arable land in standard “catchment”– productivity index for soils
How are these variables related?
Do any make sense as dependent or independent variables?
1. SIZE (ha)
2. AGLAND (km2)
3. PROD (index)
0 10 20 30 40 50 60 70 80 90 100AGLAND
20
30
40
50
60
70
80
90
100S
IZE
SIZE ~ AGLAND
r2 = .75 y = 35.4 + .66xSIZE = 35.38 + .66*AGLAND(ha) (km2)
0 10 20 30 40 50 60 70 80 90 100AGLAND
20
30
40
50
60
70
80
90
100S
IZE
residuals??
> resSize frmdat$size – (35.4 +.66 * frmdat$agland)
residual SIZE = SIZE – SIZE-hat
0.7 0.8 0.9 1.0 1.1 1.2 1.3
20
40
60
80
100
120
frmdat$prod
resS
ize
0.7 0.8 0.9 1.0 1.1 1.2 1.3PROD
20
30
40
50
60
70
80
90
100
SIZ
E
PROD & SIZE
r2 = .69SIZE = -29 + 98 * PROD
0 10 20 30 40 50 60 70 80 90 100AGLAND
20
30
40
50
60
70
80
90
100
SIZ
E
0.7 0.8 0.9 1.0 1.1 1.2 1.3PROD
20
30
40
50
60
70
80
90
100
SIZ
E
r2 = .75
r2 = .69
What have we “explained” about site
size??
size
20 40 60 80
3040
5060
7080
90
2040
6080
agland
30 50 70 90 0.7 0.8 0.9 1.0 1.1 1.2 1.3
0.7
0.8
0.9
1.0
1.1
1.2
1.3
prod
0 10 20 30 40 50 60 70 80 90 100AGLAND
0.7
0.8
0.9
1.0
1.1
1.2
1.3P
RO
D
r2 = .55
X0
X1 X2
multiple regression…
1
1 = total variance observed in independent variable (x0)
X0
201r
2011 r
X0
X1
201r
2011 r
variance in x0 explained by x1, by itself…
variance in x0 unexplained by x1…
202r
2021 r
X0
X2
variance in x0 explained by x2, by itself…
variance in x0 unexplained by x2…2
021 r
202r
)1( 202
22.01 rr
2
212
202
12020122.01
11
)(
rr
rrrr
partial correlation coefficient: proportion of variance in x0 explained by x1, that is not explained by x2…
X1
(total variance in x0 explained by x1, that is not explained by x2…)
X0
)1( 202
22.01
202
212.0 rrrR
multiple coefficient of determination: variance in x0 explained by x1 and x2, both separately, and together…
SIZE
SIZ
E
AGLAND PROD
SIZ
E
AG
LAN
D
AG
LAN
D
SIZE
PR
OD
AGLAND PROD
PR
OD
SITE-SIZE
productivity
agricultural land
SIZE = -1.8 + .42*AGLAND + 50*PROD
y = -1.8 + .42x1 + 50x2
size = -1.8 + .42*agland + 50*prod
• various scales are involved:size hectaresagland km2
prod productivity index
• increasing available agricultural land by 1 km2 increases site-size by about .4 hectares
• a 1-unit increase of soil productivity increases site-size by about 50 hectares
• which of these two factors is more important??
• calculate “beta” coefficients to eliminate the effect differing scales…
• convert the variables to Z-scores– mean of 0 – standard deviation of 1
• repeat multiple correlation analysis…
with(frmdat, {Bsize (size-mean(size))/sd(size)Bagland (agland-mean(agland))/sd(agland)Bprod (prod-mean(prod))/sd(prod) })
lmBeta lm(Bsize ~ Bagland + Bprod)
size = .55*agland + .43*prod
doesn’t change…should be zero…
site size
productivity
agricultural land
=.55
=.45
r2=.83
r2=.55
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